Chiari type 1 malformation (CMI) is a congenital anomaly characterized by the herniation of the tonsils of the cerebellum into the top of the spinal column. CMI could affect as many as 1 in 1280 people and includes varied symptoms such as severe headaches, sensory disruptions, and cardiac abnormalities. It is estimated that 65-80% of CMI patients develop syringomyelia, a fluid filled cyst in the spinal cord that can lead to nerve damage including loss of motor control. Because Chiari type I malformation is only diagnosed by magnetic resonance imaging (MRI), research into its etiology is only beginning; thus, given its frequency, this condition is vastly understudied. Highly invasive surgery is the only treatment for CMI with only 40-60% of treated patients showing improvement in their symptoms. Familial aggregation studies, including concordant twins, and cosegregating genetic conditions support a genetic component to CMI etiology. Currently, the predominant theory for etiology is a too small posterior fossa, but the genetic component behind this theory is unclear. Identifying an underlying gene and/or genes will aide identification of high-risk individuals for earlier interventions, and this work will support the development of targeted therapeutics to treat the chronic, often intractable, pain associated with this condition. Through a variety of preliminary studies, we have established that there is an underlying genetic basis for at least a subset of non-syndromic Chiari type I malformations. Furthermore, an initial genomic screen on a relatively small group of families demonstrated two primary regions of interest. Based on these findings, we propose to continue investigating the hypothesis that some non-syndromic Chiari type I malformation families have an underlying genetic basis that can be identified through genetic analysis. The hypothesis will be tested and expanded by performing a high density whole genome association screen on our CMI family cohort to confirm and further narrow previous regions of genomic region(s) of interest, fine mapping to identify the minimum candidate interval, and testing candidate genes for evidence of disease-associated variation.